Triple
T35547724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worshipful Company of Wheelwrights |
E1027264
|
entity |
| Predicate | grantedLiveryStatusInYear |
P175899
|
FINISHED |
| Object | 1670 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 1670 | Statement: [Worshipful Company of Wheelwrights, grantedLiveryStatusInYear, 1670]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grantedLiveryStatusInYear Context triple: [Worshipful Company of Wheelwrights, grantedLiveryStatusInYear, 1670]
-
A.
yearGainedLiveryStatus
chosen
Indicates the specific year in which an entity obtained or was granted livery status.
-
B.
titleGrantedInYear
Indicates that a particular title was formally granted or conferred in a specified year.
-
C.
grantedTerritoryYear
Indicates the year in which a specific territory was formally granted or assigned to an entity.
-
D.
succeededInModelYearBy
Indicates that one model year is directly followed and replaced by another model year in a sequence or product line.
-
E.
testStatusYear
Indicates the status or outcome of a test as it pertains to a specific year.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e008ba08190927acd8e5e0344c8 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 3, 2026, 4:04 p.m.